title	date	tags
airflow实战总结	2018-08-30 13:16:45 -0700

介绍�

airflow是一款开源的，分布式任务调度框架，它将一个具有上下级依赖关系的工作流，组装成一个有向无环图。
特点:
- 分布式任务调度：允许一个工作流的task在多台worker上同时执行
- 可构建任务依赖：以有向无环图的方式构建任务依赖关系
- task原子性：工作流上每个task都是原子可重试的，一个工作流某个环节的task失败可自动或手动进行重试，不必从头开始任务
工作流示意图
- 一个dag�表示一个定时的工作流�，�包含一个或者多个具有依赖关系的task
task依赖图
架构图及集群角色
- webserver� : 提供web端服务�，以及会定时生成�子进程去扫描对应的目录下的dags，�并更新数据库�
- scheduler : 任务调度�服务，�根据dags生成任务，并提交到消息中间件队列中 (redis或rabbitMq)
- celery worker : �分布在不同的机器上，作为任务真正的的执行节点。通过监听�消息中间件: redis或rabbitMq 领取任务�
- flower : 监控worker进程的存活性，启动或关闭worker进程，查看运行的task

实战

构建docker镜像

采用的airflow是未发行的1.10.0版本，原因是从1.10.0开始，支持时区的设置，而不是统一的UTC

//self.registry.domain 为docker私有镜像仓库
//self.mvn.registry.com maven 私有镜像仓库
//data0 为数据目录，data1为日志目录，运维统一配置日志清楚策略
#docker build --network host -t self.registry.domain/airflow_base_1.10.7:1.0.0 .
FROM self.registry.domain/airflow/centos_base_7.4.1708:1.0.0
LABEL AIRFLOW=1.10.7

ARG CELERY_REDIS=4.1.1
ARG DOCKER_VERSION=1.13.1
ARG AIRFLOW_VERSION=1.10.7

ADD sbin /data0/airflow/sbin

ENV SLUGIFY_USES_TEXT_UNIDECODE=yes \
    #如果构建镜像的机器需要代理才能连接外网的话，配置https_proxy
    https_proxy=https://ip:port 

RUN curl http://self.mvn.registry.com/python/python-3.5.6.jar -o /tmp/Python-3.5.6.tgz && \
    curl http://self.mvn.registry.com/airflow/${AIRFLOW_VERSION}/airflow-${AIRFLOW_VERSION}.jar -o /tmp/incubator-airflow-${AIRFLOW_VERSION}.tar.gz && \
    curl http:/self.mvn.registry.com/docker/${DOCKER_VERSION}/docker-${DOCKER_VERSION}.jar -o /tmp/docker-${DOCKER_VERSION}.tar.gz && \
    tar zxf /tmp/docker-${DOCKER_VERSION}.tar.gz -C /data0/software && \
    tar zxf /tmp/Python-3.5.6.tgz -C /data0/software && \
    tar zxf /tmp/incubator-airflow-${AIRFLOW_VERSION}.tar.gz -C /data0/software && \
    yum install -y libtool-ltdl policycoreutils-python && \
    rpm -ivh --force --nodeps /data0/software/docker-${DOCKER_VERSION}/docker-engine-selinux-${DOCKER_VERSION}-1.el7.centos.noarch.rpm && \
    rpm -ivh --force --nodeps /data0/software/docker-${DOCKER_VERSION}/docker-engine-${DOCKER_VERSION}-1.el7.centos.x86_64.rpm && \
    yum -y install gcc && yum -y install gcc-c++ && yum -y install make && \
    yum -y install zlib-devel mysql-devel python-devel cyrus-sasl-devel cyrus-sasl-lib libxml2-devel libxslt-devel && \
    cd /data0/software/Python-3.5.6 && ./configure && make && make install && \
    ln -sf /usr/local/bin/pip3 /usr/local/bin/pip && \
    ln -sf /usr/local/bin/python3 /usr/local/bin/python && \
    cd /data0/software/incubator-airflow-${AIRFLOW_VERSION} && python setup.py install && \
    pip install -i https://pypi.douban.com/simple/ apache-airflow[crypto,celery,hive,jdbc,mysql,hdfs,password,redis,devel_hadoop] && \
    pip install -i https://pypi.douban.com/simple/ celery[redis]==$CELERY_REDIS && \
    pip install -i https://pypi.douban.com/simple/ docutils && \
    ln -sf /usr/local/lib/python3.5/site-packages/apache_airflow-1.10.0-py3.5.egg/airflow /data0/software/airflow && \
    mkdir -p /data0/airflow/bin && \
    ln -sf /data0/airflow/sbin/airflow-200.sh /data0/airflow/bin/200.sh && \
    ln -sf /data0/airflow/sbin/airflow-503.sh /data0/airflow/bin/503.sh && \
    chown -R root:root /data0/software/ && \
    chown -R root:root /data0/airflow/ && \
    chmod -R 775 /data0/airflow/sbin/* && \
    chmod -R 775 /data0/airflow/bin/* && \
    echo 'source /data0/airflow/sbin/init-airflow.sh' >> ~/.bashrc && \
    rm -rf /tmp/* /data0/software/Python-3.5.6 /data0/software/incubator-airflow-${AIRFLOW_VERSION} /data0/software/docker-${DOCKER_VERSION}
    
ENV PATH=$PATH:/data0/software/jdk/bin:/data0/software/airflow/bin:/data0/airflow/sbin/:/data0/airflow/sbin/airflow/:/data0/airflow/bin/

WORKDIR /data0/airflow/bin/

通过docker 启动容器的话需要暴露几个端口

webserver: 8081
worker: 8793
flower: 5555
//启动示例
docker run --name airflow -it -d --privileged --net=host -p 8081:8081 -p 5555:5555 -p 8793:8793 -v /var/run/docker.sock:/var/run/docker.sock -v /data1:/data1 -v /data0/airflow:/data0/airflow self.registry.domain/airflow_1.10.7:1.0.0

airflow 升级到未release的1.10.0的版本

//如果之前用的是低版本的话，需要执行
airflow upgradedb 来更新迁移数据库的schema
//执行之前首先需要set mysql property
set global explicit_defaults_for_timestamp=1 //会提示is readonly variable
需要在my.cnf中添加这个设置:explicit_defaults_for_timestamp=1 并重启mysql
//update celery几个设置
celeryd_concurrency -> worker_concurrency
celery_result_backend -> result_backend

修改时区，以及界面上执行时间的显示(airlfow 默认界面上还是按照UTC显示)

//需要update configuration
default_timezone = Etc/GMT-8

//修改dags.html中的显示时间，使得界面上看起来方便
// jinjia2 传入转换函数，在views.py 的homeview的render中 
//（方法验证有点问题，再优化）
def utc2local(utc):
    epoch = time.mktime(utc.timetuple())
    offset = datetime.fromtimestamp(epoch) - datetime.utcfromtimestamp(epoch)
    return utc + offset
utc2local(last_run.execution_date).strftime("%Y-%m-%d %H:%M")
utc2local(last_run.start_date).strftime("%Y-%m-%d %H:%M")`

airflow plugins 定制化开发
- 官方文档
- plugin 这个没法传给worker，还是得重新分发到各个worker节点，建议打入airflow基础镜像中
- 增加operator时需要重启webserver和scheduler

由于dag的删除现在官方没有暴露直接的api,而完整的删除又牵扯到多个表，总结出删除dag的sql如下

set @dag_id = 'BAD_DAG';
delete from airflow.xcom where dag_id = @dag_id;
delete from airflow.task_instance where dag_id = @dag_id;
delete from airflow.sla_miss where dag_id = @dag_id;
delete from airflow.log where dag_id = @dag_id;
delete from airflow.job where dag_id = @dag_id;
delete from airflow.dag_run where dag_id = @dag_id;
delete from airflow.dag where dag_id = @dag_id;

自己实现的200和503脚本，用于集群统一的上下线操作

200脚本

#!/usr/bin/env bash
function usage() {
    echo -e "\n A tool used for starting airflow services
Usage: 200.sh {webserver|worker|scheduler|flower}
"
}

PORT=8081
ROLE=webserver
ENV_ARGS=""
check_alive() {
    PID=`netstat -nlpt | grep $PORT | awk '{print $7}' | awk -F "/" '{print $1}'`
    [ -n "$PID" ] && return 0 || return 1
}

check_scheduler_alive() {
    PIDS=`ps -ef | grep "/usr/local/bin/airflow scheduler" | grep "python" | awk '{print $2}'`
    [ -n "$PIDS" ] && return 0 || return 1
}

function get_host_ip(){
    local host=$(ifconfig | grep "inet " | grep "\-\->" | awk '{print $2}' | tail -1)
    if [[ -z "$host" ]]; then
        host=$(ifconfig | grep "inet " | grep "broadcast" | awk '{print $2}' | tail -1)
    fi
    echo "${host}"
}

start_service() {
    if [ $ROLE = 'scheduler' ];then
        check_scheduler_alive
    else
        check_alive
    fi
    if [ $? -ne 0 ];then
        nohup airflow $ROLE $ENV_ARGS > $BASE_LOG_DIR/$ROLE/$ROLE.log 2>&1 &
        sleep 5
        if [ $ROLE = 'scheduler' ];then
            check_scheduler_alive
        else
            check_alive
        fi
        if [ $? -ne 0 ];then
            echo "service start error"
            exit 1
        else
            echo "service start success"
            exit 0
        fi
    else
        echo "service alreay started"
        exit 0
    fi
}

function main() {
    if [ -z "${POOL}" ]; then
        echo "the environment variable POOL cannot be empty"
        exit 1
    fi
    source /data0/hcp/sbin/init-hcp.sh
    case "$1" in
        webserver)
            echo "starting airflow webserver"
            ROLE=webserver
            PORT=8081
            start_service
            ;;
        worker)
            echo "starting airflow worker"
            ROLE=worker
            PORT=8793
            local host_ip=$(get_host_ip)
            ENV_ARGS="-cn ${host_ip}@${host_ip}"
            start_service
            ;;
        flower)
            echo "starting airflow flower"
            ROLE=flower
            PORT=5555
            start_service
            ;;
        scheduler)
            echo "starting airflow scheduler"
            ROLE=scheduler
            start_service
            ;;     
        *)
            usage
            exit 1
    esac
}


main "$@"

503脚本

#!/usr/bin/env bash
function usage() {
    echo -e "\n A tool used for stop airflow services
Usage: 200.sh {webserver|worker|scheduler|flower}
"
}

function get_host_ip(){
    local host=$(ifconfig | grep "inet " | grep "\-\->" | awk '{print $2}' | tail -1)
    if [[ -z "$host" ]]; then
        host=$(ifconfig | grep "inet " | grep "broadcast" | awk '{print $2}' | tail -1)
    fi
    echo "${host}"
}

function main() {
    if [ -z "${POOL}" ]; then
        echo "the environment variable POOL cannot be empty"
        exit 1
    fi
    source /data0/hcp/sbin/init-hcp.sh
    case "$1" in
        webserver)
            echo "stopping airflow webserver"
            cat $AIRFLOW_HOME/airflow-webserver.pid | xargs kill -9
            ;;
        worker)
            echo "stopping airflow worker"
            PORT=8793
            PID=`netstat -nlpt | grep $PORT | awk '{print $7}' | awk -F "/" '{print $1}'`
            kill -9 $PID
            local host_ip=$(get_host_ip)
            ps -ef | grep celeryd | grep ${host_ip}@${host_ip} | awk '{print $2}' | xargs kill -9
            ;;
        flower)
            echo "stopping airflow flower"
            PORT=5555
            PID=`netstat -nlpt | grep $PORT | awk '{print $7}' | awk -F "/" '{print $1}'`
            kill -9 $PID
            start_service
            ;;
        scheduler)
            echo "stopping airflow scheduler"
            PID=`ps -ef | grep "/usr/local/bin/airflow scheduler" | grep "python" | awk '{print $2}'`
            kill -9 $PID
            ;;     
        *)
            usage
            exit 1
    esac
}


main "$@"

遇到的坑以及定制化解决方案

问题1: airflow worker 角色不能使用根用户启动

原因：不能用根用户启动的根本原因，在于airflow的worker直接用的celery，而celery 源码中有参数默认不能使用ROOT启动，否则将报错, 源码链接

C_FORCE_ROOT = os.environ.get('C_FORCE_ROOT', False)

ROOT_DISALLOWED = """\
Running a worker with superuser privileges when the
worker accepts messages serialized with pickle is a very bad idea!

If you really want to continue then you have to set the C_FORCE_ROOT
environment variable (but please think about this before you do).

User information: uid={uid} euid={euid} gid={gid} egid={egid}
"""

ROOT_DISCOURAGED = """\
You're running the worker with superuser privileges: this is
absolutely not recommended!

Please specify a different user using the --uid option.

User information: uid={uid} euid={euid} gid={gid} egid={egid}
"""

解决方案一：修改airlfow源码，在celery_executor.py中强制设置C_FORCE_ROOT

from celery import Celery, platforms 
在app = Celery(…)后新增 
platforms.C_FORCE_ROOT = True
重启即可

解决方案二：在容器初始化环境变量的时候，设置C_FORCE_ROOT参数，以零侵入的方式解决问题

#强制celery worker运行采用root模式
export C_FORCE_ROOT=True

问题2: docker in docker
- 在dags中以docker方式调度任务时，为了container的轻量话，不做重型的docker pull等操作，我们利用了docker cs架构的设计理念，只需要将宿主机的/var/run/docker.sock文件挂载到容器目录下即可 docker in docker 资料

问题3: 由于我们运行airlfow的机器是高配机器切分的虚机，host并非是传统的ip段，多节点执行后无法在master节点上通过worker节点提供的日志服务获取执行日志

查看celery源码(celery/celery/worker/worker.py)

from celery.utils.nodenames import default_nodename, worker_direct
self.hostname = default_nodename(hostname)
// 查看default_nodename方法
def default_nodename(hostname):
    """Return the default nodename for this process."""
    name, host = nodesplit(hostname or '')
    return nodename(name or NODENAME_DEFAULT, host or gethostname())

//默认在worker.py 的构造方法中没有传入hostname 所以在celery nodenames.py中default_nodename方法里面调用了gethostname
//可以看到gethostname的实现，调用了socket.gethostname，这个直接得到了虚拟机的host
gethostname = memoize(1, Cache=dict)(socket.gethostname)

解决方案：发现airflow worker的启动命令中其实提供了设置celery host name的参数

airflow worker -cn=ip@ip

问题4: 多个worker节点进行调度反序列化dag执行的时候，报找不到module的错误

当时考虑到文件更新的一致性，采用所有worker统一执行master下发的序列化dag的方案，而不依赖worker节点上实际的dag文件，开启这一特性操作如下

worker节点上： airflow worker -cn=ip@ip -p //-p为开关参数，意思是以master序列化的dag作为执行文件，而不是本地dag目录中的文件
master节点上： airflow scheduler -p

错误原因在于远程的worker节点上不存在实际的dag文件，反序列化的时候对于当时在dag中定义的函数或对象找不到module_name
解决方案一：在所有的worker节点上同时发布dags目录，缺点是dags一致性成问题
解决方案二：修改源码中序列化与反序列化的逻辑，主体思路还是替换掉不存在的module为main。修改如下:

//models.py 文件，对 class DagPickle(Base) 定义修改
import dill
class DagPickle(Base):
id = Column(Integer, primary_key=True)
# 修改前: pickle = Column(PickleType(pickler=dill))
pickle = Column(LargeBinary)
created_dttm = Column(UtcDateTime, default=timezone.utcnow)
pickle_hash = Column(Text)

__tablename__ = "dag_pickle"
def __init__(self, dag):
    self.dag_id = dag.dag_id
    if hasattr(dag, 'template_env'):
        dag.template_env = None
    self.pickle_hash = hash(dag)
    raw = dill.dumps(dag)
    # 修改前: self.pickle = dag
    reg_str = 'unusual_prefix_\w*{0}'.format(dag.dag_id)
    result = re.sub(str.encode(reg_str), b'__main__', raw)
    self.pickle =result

//cli.py 文件反序列化逻辑 run(args, dag=None) 函数
// 直接通过dill来反序列化二进制文件，而不是通过PickleType 的result_processor做中转
修改前: dag = dag_pickle.pickle
修改后：dag = dill.loads(dag_pickle.pickle)

解决方案三：源码零侵入，使用python的types.FunctionType重新创建一个不带module的function，这样序列化与反序列化的时候不会有问题（待验证)

new_func = types.FunctionType((lambda df: df.iloc[:, 0].size == xx).__code__, {})

问题5：由于airflow在master查看task执行日志是通过各个节点的http服务获取的，但是存入task_instance表中的host_name不是ip，可见获取hostname的方式有问题.

解决方案：修改airflow/utils/net.py 中get_hostname函数，添加优先获取环境变量中设置的hostname的逻辑

//models.py TaskInstance
self.hostname = get_hostname()
//net.py 在get_hostname里面加入一个获取环境变量的逻辑
import os
def get_hostname():
    """
    Fetch the hostname using the callable from the config or using
    `socket.getfqdn` as a fallback.
    """
    # 尝试获取环境变量
    if 'AIRFLOW_HOST_NAME' in os.environ:
        return os.environ['AIRFLOW_HOST_NAME']
    # First we attempt to fetch the callable path from the config.
    try:
        callable_path = conf.get('core', 'hostname_callable')
    except AirflowConfigException:
        callable_path = None

    # Then we handle the case when the config is missing or empty. This is the
    # default behavior.
    if not callable_path:
        return socket.getfqdn()

    # Since we have a callable path, we try to import and run it next.
    module_path, attr_name = callable_path.split(':')
    module = importlib.import_module(module_path)
    callable = getattr(module, attr_name)
    return callable()

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